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Exploring Data Aggregation in Policy Learning for vision-based Urban Autonomous Driving

Exploring Data Aggregation in Policy Learning for Vision-bas...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Prakash, Aditya Behl, Aseem Ohn-Bar, Eshed Chitta, Kashyap Geiger, Andreas Max Planck Inst Intelligent Syst Tubingen Germany Univ Tubingen Tubingen Germany Boston Univ Boston MA USA

ISBN: (纸本)9781728171685

Data aggregation techniques can significantly improve vision-based policy learning within a training environment, e.g., learning to drive in a specific simulation condition. However, as on-policy data is sequentially sampled and added in an iterative manner, the policy can specialize and overfit to the training conditions. For real-world applications, it is useful for the learned policy to generalize to novel scenarios that differ from the training conditions. To improve policy learning while maintaining robustness when training end-to-end driving policies, we perform an extensive analysis of data aggregation techniques in the CARLA environment. We demonstrate how the majority of them have poor generalization performance, and develop a novel approach with empirically better generalization performance compared to existing techniques. Our two key ideas are (1) to sample critical states from the collected on-policy data based on the utility they provide to the learned policy in terms of driving behavior, and (2) to incorporate a replay buffer which progressively focuses on the high uncertainty regions of the policy's state distribution. We evaluate the proposed approach on the CARLA NoCrash benchmark, focusing on the most challenging driving scenarios with dense pedestrian and vehicle traffic. Our approach improves driving success rate by 16% over state-of-the-art, achieving 87% of the expert performance while also reducing the collision rate by an order of magnitude without the use of any additional modality, auxiliary tasks, architectural modifications or reward from the environment.

关键词： Iterative methods

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Geometric Anchor Correspondence Mining with Uncertainty Modeling for Universal Domain Adaptation

Geometric Anchor Correspondence Mining with Uncertainty Mode...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Chen, Liang Lou, Yihang He, Jianzhong Bai, Tao Deng, Minghua Peking Univ Sch Math Sci Beijing Peoples R China Huawei Technol Intelligent Vis Dept Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Universal domain adaptation (UniDA) aims to transfer the knowledge learned from a label-rich source domain to a label-scarce target domain without any constraints on the label space. However, domain shift and category shift make UniDA extremely challenging, which mainly lies in how to recognize both shared "known" samples and private "unknown" samples. Previous works rarely explore the intrinsic geometrical relationship between the two domains, and they manually set a threshold for the overconfident closed-world classifier to reject "unknown" samples. Therefore, in this paper, we propose a Geometric anchorguided Adversarial and conTrastive learning framework with uncErtainty modeling called GATE to alleviate these issues. Specifically, we first develop a random walk-based anchor mining strategy together with a high-order attention mechanism to build correspondence across domains. Then a global joint local domain alignment paradigm is designed, i.e., geometric adversarial learning for global distribution calibration and subgraph-level contrastive learning for local region aggregation. Toward accurate target private samples detection, GATE introduces a universal incremental classifier by modeling the energy uncertainty. We further efficiently generate novel categories by manifold mixup, and minimize the open-set entropy to learn the "unknown" threshold adaptively. Extensive experiments on three benchmarks demonstrate that GATE significantly outperforms previous state-of-the-art UniDA methods.

关键词： Representation learning Manifolds Adaptation models computer vision Uncertainty Computational modeling Logic gates

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Scan2Cap: Context-aware Dense Captioning in RGB-D Scans

Scan2Cap: Context-aware Dense Captioning in RGB-D Scans

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Chen, Dave Zhenyu Gholami, Ali Niesner, Matthias Chang, Angel X. Tech Univ Munich Munich Germany Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665445092

We introduce the task of dense captioning in 3D scans from commodity RGB-D sensors. As input, we assume a point cloud of a 3D scene;the expected output is the bounding boxes along with the descriptions for the underlying objects. To address the 3D object detection and description problems, we propose Scan2Cap, an end-to-end trained method, to detect objects in the input scene and describe them in natural language. We use an attention mechanism that generates descriptive tokens while referring to the related components in the local context. To reflect object relations (i.e. relative spatial relations) in the generated captions, we use a message passing graph module to facilitate learning object relation features. Our method can effectively localize and describe 3D objects in scenes from the ScanRefer dataset, outperforming 2D baseline methods by a significant margin (27.61% CiDEr@0.5IoU improvement).

关键词： Location awareness Three-dimensional displays Message passing Natural languages Pipelines computer architecture Object detection

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Hot-started NAS for Task-specific Embedded Applications

Hot-started NAS for Task-specific Embedded Applications

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Hendrickx, Lotte Van Ranst, Wiebe Goedeme, Toon Katholieke Univ Leuven EAVISE PSI ESAT Jan Pieter De Nayerlaan 5 B-2860 St Katelijne Waver Belgium

ISBN: (纸本)9781665487399

Neural architecture search (NAS) has proven its worth in discovering new neural networks. Combining the possibility to satisfy multiple objectives in one search, it is especially useful for getting the most out of embedded devices with limited resources. However, research into small and efficient neural networks precedes NAS. We investigate the influence of combining this pre-existing knowledge with NAS techniques, for which we propose to hot-start the NAS search with a human-designed optimal network. Our experiments show that doing so speeds up the NAS process significantly, but the resulting optimal model at the end is only marginally better. Since embedded devices are often used for a specific task, we also explore the impact of using a task-specific dataset in the NAS process. Our experiments demonstrate that for a constrained problem, a smaller network can be found as compared to a general problem.

关键词： Knowledge engineering computer vision conferences Neural networks Size measurement Search problems pattern recognition

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Disentangled Loss for Low-Bit Quantization-Aware Training

Disentangled Loss for Low-Bit Quantization-Aware Training

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Allenet, Thibault Briand, David Bichler, Olivier Sentieys, Olivier CEA LIST Saclay France Univ Rennes INRIA Rennes France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Quantization-Aware Training (QAT) has recently showed a lot of potential for low-bit settings in the context of image classification. Approaches based on QAT are using the Cross Entropy Loss function which is the reference loss function in this domain. We investigate quantization-aware training with disentangled loss functions. We qualify a loss to disentangle as it encourages the network output space to be easily discriminated with linear functions. We introduce a new method, Disentangled Loss Quantization Aware Training, as our tool to empirically demonstrate that the quantization procedure benefits from those loss functions. Results show that the proposed method substantially reduces the loss in top-1 accuracy for low-bit quantization on CIFAR10, CIFAR100 and ImageNet. Our best result brings the top-1 Accuracy of a Resnet-18 from 63.1% to 64.0% with binary weights and 2-bit activations when trained on ImageNet.

关键词： Training Visualization computer vision Quantization (signal) conferences Entropy pattern recognition

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SketchEdit: Mask-Free Local Image Manipulation with Partial Sketches

SketchEdit: Mask-Free Local Image Manipulation with Partial ...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Zeng, Yu Lin, Zhe Patel, Vishal M. Johns Hopkins Univ Baltimore MD 21218 USA Adobe Res San Jose CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Sketch-based image manipulation is an interactive image editing task to modify an image based on input sketches from users. Existing methods typically formulate this task as a conditional inpainting problem, which requires users to draw an extra mask indicating the region to modify in addition to sketches. The masked regions are regarded as holes and filled by an inpainting model conditioned on the sketch. With this formulation, paired training data can be easily obtained by randomly creating masks and extracting edges or contours. Although this setup simplifies data preparation and model design, it complicates user interaction and discards useful information in masked regions. To this end, we investigate a new paradigm of sketch-based image manipulation: maskfree local image manipulation, which only requires sketch inputs from users and utilizes the entire original image. Given an image and sketch, our model automatically predicts the target modification region and encodes it into a structure agnostic style vector. A generator then synthesizes the new image content based on the style vector and sketch. The manipulated image is finally produced by blending the generator output into the modification region of the original image. Our model can be trained in a self-supervised fashion by learning the reconstruction of an image region from the style vector and sketch. The proposed method offers simpler and more intuitive user workflows for sketch-based image manipulation and provides better results than previous approaches. More results, code and interactive demo will be available at https://***/sketchedit.

关键词： computer vision Codes Image edge detection Training data Predictive models Generators Data models

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End-to-End Semi-Supervised Learning for Video Action Detection

End-to-End Semi-Supervised Learning for Video Action Detecti...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Kumar, Akash Rawat, Yogesh Singh Univ Cent Florida Ctr Res Comp Vis Orlando FL 32816 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this work, we focus on semi-supervised learning for video action detection which utilizes both labeled as well as unlabeled data. We propose a simple end-to-end consistency based approach which effectively utilizes the unlabeled data. Video action detection requires both, action class prediction as well as a spatio-temporal localization of actions. Therefore, we investigate two types of constraints, classification consistency, and spatio-temporal consistency. The presence of predominant background and static regions in a video makes it challenging to utilize spatio-temporal consistency for action detection. To address this, we propose two novel regularization constraints for spatio-temporal consistency;1) temporal coherency, and 2) gradient smoothness. Both these aspects exploit the temporal continuity of action in videos and are found to be effective for utilizing unlabeled videos for action detection. We demonstrate the effectiveness of the proposed approach on two different action detection benchmark datasets, UCF101-24 and JHMDB-21. In addition, we also show the effectiveness of the proposed approach for video object segmentation on the Youtube-VOS which demonstrates its generalization capability The proposed approach achieves competitive performance by using merely 20% of annotations on UCF101-24 when compared with recent fully supervised methods. On UCF101-24, it improves the score by +8.9% and +11% at 0.5 f-mAP and v-mAP respectively, compared to supervised approach. The code and models will be made publicly available at: https://***/AKASH2907/End-to-End-Semi-Supervised-Learning-for-Video-Action-Detection.

关键词： Location awareness computer vision Codes Annotations Object segmentation Semisupervised learning Benchmark testing

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Improved Noise2Noise Denoising with Limited Data

Improved Noise2Noise Denoising with Limited Data

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Calvarons, Adria Font Tech Univ Munich Munich Germany

ISBN: (纸本)9781665448994

Deep learning methods have proven to be very effective for the task of image denoising even when clean reference images are not available. In particular, Noise2Noise, which requires pairs of noisy images during the training phase, has been shown to yield results as good as approaches using pairs of noisy and clean images (Noise2Clean). However, the performance of Noise2Noise drops when the amount of training data is reduced, limiting its capability in practical scenarios. In this work, an analysis of the Noise2Noise learning strategy is done using real noise and synthetic datasets. This paper demonstrates, using diverse network architectures and loss functions, that the duplicity of information in the noisy pairs can be exploited to reach increased denoising performance of Noise2Noise. Additionally, the issue of overfitting in Noise2Noise is analyzed, given its relevance when training with limited data, and an interpretable early termination criterion is proposed.

关键词： Training Limiting Image color analysis Noise reduction Training data Network architecture pattern recognition

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Gradually Vanishing Bridge for Adversarial Domain Adaptation

Gradually Vanishing Bridge for Adversarial Domain Adaptation

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Cui, Shuhao Wang, Shuhui Zhuo, Junbao Su, Chi Huang, Qingming Tian, Qi Chinese Acad Sci Inst Comput Tech Key Lab Intell Info Proc Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Kingsoft Cloud Beijing Peoples R China Peng Cheng Lab Shenzhen Peoples R China Noahs Ark Lab Huawei Technol Beijing Peoples R China

ISBN: (纸本)9781728171685

In unsupervised domain adaptation, rich domain-specific characteristics bring great challenge to learn domain-invariant representations. However, domain discrepancy is considered to be directly minimized in existing solutions, which is difficult to achieve in practice. Some methods alleviate the difficulty by explicitly modeling domain-invariant and domain-specific parts in the representations, but the adverse influence of the explicit construction lies in the residual domain-specific characteristics in the constructed domain-invariant representations. In this paper, we equip adversarial domain adaptation with Gradually Vanishing Bridge (GVB) mechanism on both generator and discriminator. On the generator, GVB could not only reduce the overall transfer difficulty, but also reduce the influence of the residual domain-specific characteristics in domain-invariant representations. On the discriminator, GVB contributes to enhance the discriminating ability, and balance the adversarial training process. Experiments on three challenging datasets show that our GVB methods outperform strong competitors, and cooperate well with other adversarial methods. The code is available at https://***/cuishuhao/GVB.

关键词： computer vision

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Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentation

Dynamic Prototype Convolution Network for Few-Shot Semantic ...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Liu, Jie Bao, Yanqi Xie, Guo-Sen Xiong, Huan Sonke, Jan-Jakob Gavves, Efstratios Univ Amsterdam Amsterdam Netherlands Northeastern Univ Shenyang Peoples R China Netherlands Canc Inst Amsterdam Netherlands Nanjing Univ Sci & Technol Nanjing Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The key challenge for few-shot semantic segmentation (FSS) is how to tailor a desirable interaction among support and query features and/or their prototypes, under the episodic training scenario. Most existing FSS methods implement such support/query interactions by solely leveraging plain operations - e.g., cosine similarity and feature concatenation - for segmenting the query objects. However, these interaction approaches usually cannot well capture the intrinsic object details in the query images that are widely encountered in FSS, e.g., if the query object to be segmented has holes and slots, inaccurate segmentation almost always happens. To this end, we propose a dynamic prototype convolution network (DPCN) to fully capture the aforementioned intrinsic details for accurate FSS. Specifically, in DPCN, a dynamic convolution module (DCM) is firstly proposed to generate dynamic kernels from support foreground, then information interaction is achieved by convolution operations over query features using these kernels. Moreover, we equip DPCN with a support activation module (SAM) and a feature filtering module (FFM) to generate pseudo mask and filter out background information for the query images, respectively. SAM and FFM together can mine enriched context information from the query features. Our DPCN is also flexible and efficient under the k-shot FSS setting. Extensive experiments on PASCAL-5(i) and COCO-20(i) show that DPCN yields superior performances under both 1-shot and 5-shot settings.

关键词： Training Image segmentation computer vision Convolution Semantics Prototypes Information filters

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